基于生理信号的医疗保健应用深度学习：综述。
Deep learning for healthcare applications based on physiological signals: A review.
机构信息
Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom.
Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore.
出版信息
Comput Methods Programs Biomed. 2018 Jul;161:1-13. doi: 10.1016/j.cmpb.2018.04.005. Epub 2018 Apr 11.
BACKGROUND AND OBJECTIVE
We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.
METHODS
An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.
RESULTS
During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.
CONCLUSIONS
This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.
背景与目的
我们在知识的海洋中撒网，检索了关于生理信号深度学习方法的最新科学研究。我们找到了 53 篇关于这个主题的研究论文，发表日期为 2008 年 1 月 1 日至 2017 年 12 月 31 日。
方法
初步的文献计量分析表明，所回顾的论文集中在肌电图（EMG）、脑电图（EEG）、心电图（ECG）和眼电图（EOG）上。这四类被用来构建后续的内容回顾。
结果
在内容回顾中，我们了解到深度学习在处理大数据集和多样化数据集方面比经典分析和机器分类方法表现更好。深度学习算法试图通过使用所有可用的输入来开发模型。
结论
这篇综述文章描述了到目前为止使用的各种深度学习算法的应用，但在未来，它将用于更多的医疗保健领域，以提高诊断质量。
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